Artificial Neural Networks for Energy Management System. Applicability and limitations of the main paradigms

Gonzalo Joya (Université de Malaga)

Résumé : Electrical energy has obviously become an essential element for the operation and development of current society. Consequently, the improvement of the set of tasks implicated in its management - what we call Energy Management System (EMS)- constitutes a high-priority research field from the social, economical and human points of view. These tasks, which may be grouped as forecasting, state estimation and security related tasks, present all or most of the following characteristics : 1) their solution involves a high number of noisy and/or incomplete data. 2) Complex relationships exist among the variables implicated in each problem. 3) They are difficult to handle by an operator. 4) It is difficult to find a numerical or algorithmical solution to the problem, and if this solution is found, it presents a high computational cost. 5) They cannot be described by means of a simple set of rules based on the expert’s knowledge. 6) Real time operation is frequently required. These features discourage the application of classical numerical methods, whereas Artificial Neural Networks (ANN) based techniques turn out to be especially well suited for them. Besides, many of these problems may be approached as either a classification or a function approximation problem, and both approaches fit into the different paradigms that ANN techniques comprise. Thus, on one hand, feed-forward supervised neural networks may be used to obtain a particular numerical function. On the other hand, unsupervised neural networks take advantage of their ability to extract unknown criteria from a pattern set to achieve a visual classification of the patterns.
Yet ANNs are often improperly used and they are required to solve problems that they are not prepared for. This spurious usage is partly due to the complex internal representation of the network parameters, but these parameters are easily obtained by means of well-established training algorithms. Thus, we are tempted to use ANNs not only as "black boxes" but as some kind of "magic boxes". This risk justifies a deep study of the internal behavior of ANNs.

In this course we review the application of ANNs for EMS from a double perspective. On one hand, we will study the most significant operations on an EMS. From their features and the limitations of the classical solutions, we will justify a neural solution and the choice of the most appropriate neural paradigm. On the other hand, we will use the EMS environment as a "benchmark" to highlight the main features, limitations and usage recommendations of the mostly applied neural paradigms.